Skip to main content
SpringerLink
Log in

Detection of speaker liveness with CNN isolated word ASR for verification systems

  • 1180: Cybersecurity, Intelligent Multimedia Systems for Threat Detection and Data Protection
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The article proposes a new speaker liveness test for speech verification systems. Biometric authentication systems based on speaker verification are often subject to presentation attacks which use the target speaker’s recorded speech. We propose a liveness test which uses CNN isolated word ASR as a countermeasure to repel attacks during the verification process. The liveness test incorporates the extraction of MFCC coefficients and the CNN classifier. Reliability of the recognition of isolated words is verified against a validation dataset of various sizes. The achieved results verified the system’s reliability, which decreased slightly as the size of the keyword dataset increased. The proposed method represents a simple and effective security component against presentation attacks for existing SV systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abu Shariah MAM, Ainon RN, Zainuddin R, Khalifa OO (2007) Human computer interaction using isolated-words speech recognition technology. In: 2007 international conference on intelligent and advanced systems, pp 1173–1178

  2. Chen G, Parada C, Heigold G (2014) Small-footprint keyword spotting using deep neural networks. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4087–4091

  3. Dehak N, Kenny PJ, Dehak R, Dumouchel P, Ouellet P (2011) Front-end factor analysis for speaker verification. IEEE Trans Audio Speech Language Process 19(4):788–798

    Article  Google Scholar 

  4. Dhanashri D, Dhonde SB (2017) Isolated word speech recognition system using deep neural networks. In: Satapathy SC, Bhateja V, Joshi A (eds) Proceedings of the international conference on data engineering and communication technology. Springer, Singapore, pp 9–17

  5. Dörfler M, Bammer R, Grill T (2017) Inside the spectrogram Convolutional neural networks in audio processing. In: 2017 international conference on sampling theory and applications (SampTA), pp 152–155

  6. Fang F, Yamagishi J, Echizen I, Sahidullah MD, Kinnunen T (2018) Transforming acoustic characteristics to deceive playback spoofing countermeasures of speaker verification systems. arXiv:1809.04274

  7. Frangoulis E (1991) Isolated word recognition in noisy environment by vector quantization of the hmm and noise distributions. In: Proceedings ICASSP 91: 1991 International conference on acoustics, Speech, and Signal Processing, vol 1, pp 413–416

  8. Fu S, Hu T, Tsao Y, Lu X (2017) Complex spectrogram enhancement by convolutional neural network with multi-metrics learning. In: 2017 IEEE 27th international workshop on machine learning for signal processing (MLSP), pp 1–6

  9. Garcia-Romero D, Espy-Wilson C (2011) Analysis of i-vector length normalization in speaker recognition systems. 249–252, 01

  10. Gouda SK, Kanetkar S, Harrison D, Warmuth MK (2018) Speech recognition: Keyword spotting through image recognition

  11. Imtiaz MA, Raja G (2016) Isolated word automatic speech recognition (asr) system using mfcc, dtw knn. In: 2016 asia pacific conference on multimedia and broadcasting (APMediaCast), pp 106–110

  12. Jia Y, Zhang Y, Weiss RJ, Wang Q, Shen J, Ren F, Chen Z, Nguyen P, Pang R, Lopez-Moreno I, Wu Y (2018) Transfer learning from speaker verification to multispeaker text-to-speech synthesis. arXiv:1806.04558

  13. Kenny P, Boulianne G, Ouellet P, Dumouchel P (2007) Joint factor analysis versus eigenchannels in speaker recognition. Audio Speech, and Language Processing, IEEE Transactions 15:1435–1447, 06

    Article  Google Scholar 

  14. Li X, Zhou Z (2017) Speech command recognition with convolutional neural network CS229 Stanford education

  15. Partila P, Tovarek J, Ilk GH, Rozhon J, Voznak M (2020) Deep learning serves voice cloning: How vulnerable are automatic speaker verification systems to spoofing trials? IEEE Commun Mag 58(2):100–105

    Article  Google Scholar 

  16. Ping W, Peng K, Gibiansky A, Arik SO, Kannan A, Narang S, Raiman J, Miller J (2017) Deep voice 3: Scaling text-to-speech with convolutional sequence learning. arXiv:1710.07654

  17. Poddar A, Sahidullah M, Saha G (2017) Improved i-vector extraction technique for speaker verification with short utterances. Int J Speech Technol 11

  18. Ranjan R, Dubey RK (2016) Isolated word recognition using hmm for maithili dialect. In: 2016 international conference on signal processing and communication (ICSC), pp 323–327

  19. Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted gaussian mixture models. Digit Signal Process 10(1):19–41

    Article  Google Scholar 

  20. Singhal S, Dubey RK (2015) Automatic speech recognition for connected words using dtw/hmm for english/ hindi languages. In: 2015 communication control and intelligent systems (CCIS), pp 199–203

  21. Slívová M, Partila P, Továrek J, Voznák M (2020) Isolated word automatic speech recognition system. In: Multimedia communications services and security, pp 252–264

  22. Tang R, Lin J (2018) Deep residual learning for small-footprint keyword spotting. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5484–5488

  23. Tropea M, Fedele G (2019) Classifiers comparison for convolutional neural networks (cnns) in image classification. In: 2019 IEEE/ACM 23rd international symposium on distributed simulation and real time applications (DS-RT), pp 1–4

  24. Warden P (2018) Speech commands: A dataset for limited-vocabulary speech recognition. arXiv:1804.03209

  25. Zhang Y, Suda N, Lai L, Chandra V (2018) Hello edge: Keyword spotting on microcontrollers

  26. Zhao L, Han Z (2010) Speech recognition system based on integrating feature and hmm. In: 2010 international conference on measuring technology and mechatronics automation, vol 3, pp 449–452

Download references

Acknowledgements

The research leading to this results was supported by Czech Ministry of Education, Youth and Sports within project reg. no. SP2021/25 and also partially within the Large Infrastructures for Research, Experimental Development and Innovations project ”e-Infrastructure CZ” reg. no. LM2018140, both projects were conducted by VSB-Technical university of Ostrava.

Author information

Authors and Affiliations

  1. VSB – Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, 17. listopadu 2172/15, 708 00, Ostrava-Poruba, Czech Republic

    Martina Slivova, Miroslav Voznak, Jaromir Tovarek & Pavol Partila

Authors
  1. Martina Slivova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miroslav Voznak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaromir Tovarek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pavol Partila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martina Slivova.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Slivova, M., Voznak, M., Tovarek, J. et al. Detection of speaker liveness with CNN isolated word ASR for verification systems. Multimed Tools Appl 81, 9445–9457 (2022). https://doi.org/10.1007/s11042-021-11150-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11150-1

Keywords

Search

Navigation